283 research outputs found
Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0
Stuttering is a varied speech disorder that harms an individual's
communication ability. Persons who stutter (PWS) often use speech therapy to
cope with their condition. Improving speech recognition systems for people with
such non-typical speech or tracking the effectiveness of speech therapy would
require systems that can detect dysfluencies while at the same time being able
to detect speech techniques acquired in therapy. This paper shows that
fine-tuning wav2vec 2.0 [1] for the classification of stuttering on a sizeable
English corpus containing stuttered speech, in conjunction with multi-task
learning, boosts the effectiveness of the general-purpose wav2vec 2.0 features
for detecting stuttering in speech; both within and across languages. We
evaluate our method on FluencyBank , [2] and the German therapy-centric Kassel
State of Fluency (KSoF) [3] dataset by training Support Vector Machine
classifiers using features extracted from the finetuned models for six
different stuttering-related event types: blocks, prolongations, sound
repetitions, word repetitions, interjections, and - specific to therapy -
speech modifications. Using embeddings from the fine-tuned models leads to
relative classification performance gains up to 27% w.r.t. F1-score.Comment: Accepted at Interspeech 202
Speaker Adaptation for End-To-End Speech Recognition Systems in Noisy Environments
We analyze the impact of speaker adaptation in end-to-end automatic speech
recognition models based on transformers and wav2vec 2.0 under different noise
conditions. By including speaker embeddings obtained from x-vector and
ECAPA-TDNN systems, as well as i-vectors, we achieve relative word error rate
improvements of up to 16.3% on LibriSpeech and up to 14.5% on Switchboard. We
show that the proven method of concatenating speaker vectors to the acoustic
features and supplying them as auxiliary model inputs remains a viable option
to increase the robustness of end-to-end architectures. The effect on
transformer models is stronger, when more noise is added to the input speech.
The most substantial benefits for systems based on wav2vec 2.0 are achieved
under moderate or no noise conditions. Both x-vectors and ECAPA-TDNN embeddings
outperform i-vectors as speaker representations. The optimal embedding size
depends on the dataset and also varies with the noise condition.Comment: Accepted at ASRU 202
Electrostatic extraction of cold molecules from a cryogenic reservoir
We present a method which delivers a continuous, high-density beam of slow
and internally cold polar molecules. In our source, warm molecules are first
cooled by collisions with a cryogenic helium buffer gas. Cold molecules are
then extracted by means of an electrostatic quadrupole guide. For ND the
source produces fluxes up to molecules/s with
peak densities up to molecules/cm. For
HCO the population of rovibrational states is monitored by depletion
spectroscopy, resulting in single-state populations up to .Comment: 4 pages, 4 figures, changes to the text, updated figures and
reference
Fullerene-based Biocomponents : New Concepts For Functionalising Membranes
Lipophilic hexakisadducts of fullerene C60 form unprecedented rod-like nanoaggregates in phospholipid-membrane bilayers, resulting in modification of the micromechanic properties and stabilisation of the membrane. Lipofullerenes with amphiphilic side chains enable additionally derivatisation and molecular recognition at the membrane surface. The amphiphilic spacer acts as a transmembrane anchor and provides the terminal functionality outside of the membrane. New systems derived from parent compound 3 carry two functional groups each and can be easily modified due to the modular synthesis. Terminal functionalities to be investigated include D(+)-biotin and IDA (iminodiacetic acid) ligands, as used in nickel-histidine tags. Modification of the lipophilic region, for instance with unsaturated addends is also possible. These addends should allow polymerisation inside the membrane and potentially lead to a tremendous increase of the membrane rigidity. Furthermore, mono- and bilayer-forming fullerene derivatives without the membrane-forming support of lecithins are investigated and exhibit interesting features
Surveillance and falsification implications for open source intelligence investigations
© 2015 ACM. Legitimacy of surveillance is crucial to safeguarding validity of OSINT data as a tool for law-enforcement agencies
A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem
Most stuttering detection and classification research has viewed stuttering
as a multi-class classification problem or a binary detection task for each
dysfluency type; however, this does not match the nature of stuttering, in
which one dysfluency seldom comes alone but rather co-occurs with others. This
paper explores multi-language and cross-corpus end-to-end stuttering detection
as a multi-label problem using a modified wav2vec 2.0 system with an
attention-based classification head and multi-task learning. We evaluate the
method using combinations of three datasets containing English and German
stuttered speech, one containing speech modified by fluency shaping. The
experimental results and an error analysis show that multi-label stuttering
detection systems trained on cross-corpus and multi-language data achieve
competitive results but performance on samples with multiple labels stays below
over-all detection results.Comment: Accepted for presentation at Interspeech 2023. arXiv admin note:
substantial text overlap with arXiv:2210.1598
Multi-class Detection of Pathological Speech with Latent Features: How does it perform on unseen data?
The detection of pathologies from speech features is usually defined as a
binary classification task with one class representing a specific pathology and
the other class representing healthy speech. In this work, we train neural
networks, large margin classifiers, and tree boosting machines to distinguish
between four different pathologies: Parkinson's disease, laryngeal cancer,
cleft lip and palate, and oral squamous cell carcinoma. We demonstrate that
latent representations extracted at different layers of a pre-trained wav2vec
2.0 system can be effectively used to classify these types of pathological
voices. We evaluate the robustness of our classifiers by adding room impulse
responses to the test data and by applying them to unseen speech corpora. Our
approach achieves unweighted average F1-Scores between 74.1% and 96.4%,
depending on the model and the noise conditions used. The systems generalize
and perform well on unseen data of healthy speakers sampled from a variety of
different sources.Comment: Submitted to ICASSP 202
Storage and Adiabatic Cooling of Polar Molecules in a Microstructured Trap
We present a versatile electric trap for the exploration of a wide range of
quantum phenomena in the interaction between polar molecules. The trap combines
tunable fields, homogeneous over most of the trap volume, with steep gradient
fields at the trap boundary. An initial sample of up to 10^8 CH3F molecules is
trapped for as long as 60 seconds, with a 1/e storage time of 12 seconds.
Adiabatic cooling down to 120 mK is achieved by slowly expanding the trap
volume. The trap combines all ingredients for opto-electrical cooling, which,
together with the extraordinarily long storage times, brings field-controlled
quantum-mechanical collision and reaction experiments within reach
Classifying Dementia in the Presence of Depression: A Cross-Corpus Study
Automated dementia screening enables early detection and intervention,
reducing costs to healthcare systems and increasing quality of life for those
affected. Depression has shared symptoms with dementia, adding complexity to
diagnoses. The research focus so far has been on binary classification of
dementia (DEM) and healthy controls (HC) using speech from picture description
tests from a single dataset. In this work, we apply established baseline
systems to discriminate cognitive impairment in speech from the semantic Verbal
Fluency Test and the Boston Naming Test using text, audio and emotion
embeddings in a 3-class classification problem (HC vs. MCI vs. DEM). We perform
cross-corpus and mixed-corpus experiments on two independently recorded German
datasets to investigate generalization to larger populations and different
recording conditions. In a detailed error analysis, we look at depression as a
secondary diagnosis to understand what our classifiers actually learn.Comment: Accepted at INTERSPEECH 202
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